While rapid progress has been made in developing datasets that combine image and text, in contrast, only a few datasets that pair language and 3D data exist. Text2Shape[1]} released a dataset of 15,038 chairs and tables from ShapeNet each with around 5 text captions, giving 75,344 total text-shape pairs. ShapeGlot[2]} released the CiC (Chairs in Context) dataset which contains 4,511 chairs from ShapeNet along with 78,789 descriptive utterances generated from a referential game. Due to the small scale and limited diversity of these datasets, current SoTA text-to-3D models[3]}, [4]}, [5]} forgo the use of 3D datasets entirely and instead rely on 2D image-text supervision.
